{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_datareader import data\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(style='darkgrid', context='talk', palette='Paired')\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Stock Data with Yahoo Finance (Old way)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ImmediateDeprecationError",
     "evalue": "\nYahoo Daily has been immediately deprecated due to large breaks in the API without the\nintroduction of a stable replacement. Pull Requests to re-enable these data\nconnectors are welcome.\n\nSee https://github.com/pydata/pandas-datareader/issues\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImmediateDeprecationError\u001b[0m                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-68d193165306>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;31m# User pandas_reader.data.DataReader to load the desired data. As simple as that.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mpanel_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'INPX'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_source\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart_date\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend_date\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas_datareader\\data.py\u001b[0m in \u001b[0;36mDataReader\u001b[1;34m(name, data_source, start, end, retry_count, pause, session, access_key)\u001b[0m\n\u001b[0;32m    308\u001b[0m     \"\"\"\n\u001b[0;32m    309\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdata_source\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"yahoo\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 310\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mImmediateDeprecationError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDEP_ERROR_MSG\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Yahoo Daily'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    311\u001b[0m         return YahooDailyReader(symbols=name, start=start, end=end,\n\u001b[0;32m    312\u001b[0m                                 \u001b[0madjust_price\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImmediateDeprecationError\u001b[0m: \nYahoo Daily has been immediately deprecated due to large breaks in the API without the\nintroduction of a stable replacement. Pull Requests to re-enable these data\nconnectors are welcome.\n\nSee https://github.com/pydata/pandas-datareader/issues\n"
     ]
    }
   ],
   "source": [
    "# Define the instruments to download. We would like to see Apple, Microsoft and the S&P500 index.\n",
    "tickers = ['AAPL', 'MSFT', '^GSPC']\n",
    "\n",
    "# We would like all available data from 01/01/2000 until 12/31/2016.\n",
    "start_date = '2010-01-01'\n",
    "end_date = '2016-12-31'\n",
    "\n",
    "# User pandas_reader.data.DataReader to load the desired data. As simple as that.\n",
    "panel_data = data.DataReader(tickers, 'yahoo', start_date, end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Updated (4/14/18): Getting Stock Data with Google Finance\n",
    "Yahoo finance has changed the structure of its website and as a result the most popular Python packages for retrieving data have stopped functioning properly. Until this is resolved, the following piece of code will provide sufficient data to run the examples in this series of articles. Data is now taken from Google Finance and we are using the ETF \"SPY\" as proxy for S&P 500 on Google Finance.\n",
    "\n",
    "Please not that if you use this set of data to run the example, you may be getting slightly different results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the instruments to download. We would like to see Apple, Microsoft and the S&P500 index.\n",
    "tickers = ['AAPL', 'MSFT', 'SPY']\n",
    "\n",
    "# We would like all available data from 01/01/2000 until 12/31/2016.\n",
    "start_date = '2010-01-01'\n",
    "end_date = '2016-12-31'\n",
    "\n",
    "# Use pandas_reader.data.DataReader to load the desired data. As simple as that.\n",
    "data = data.DataReader(tickers, 'morningstar', start_date, end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">AAPL</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>30.1046</td>\n",
       "      <td>30.4786</td>\n",
       "      <td>30.0800</td>\n",
       "      <td>30.4443</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>30.5729</td>\n",
       "      <td>30.6429</td>\n",
       "      <td>30.3400</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>123432050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.6257</td>\n",
       "      <td>30.7986</td>\n",
       "      <td>30.4643</td>\n",
       "      <td>30.6843</td>\n",
       "      <td>150476004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.1386</td>\n",
       "      <td>30.7471</td>\n",
       "      <td>30.1071</td>\n",
       "      <td>30.6257</td>\n",
       "      <td>138039594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.0829</td>\n",
       "      <td>30.2857</td>\n",
       "      <td>29.8643</td>\n",
       "      <td>30.2400</td>\n",
       "      <td>119282324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.2829</td>\n",
       "      <td>30.2857</td>\n",
       "      <td>29.8657</td>\n",
       "      <td>30.0571</td>\n",
       "      <td>111969081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>30.0157</td>\n",
       "      <td>30.4286</td>\n",
       "      <td>29.7786</td>\n",
       "      <td>30.4243</td>\n",
       "      <td>115557365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-12</th>\n",
       "      <td>29.6743</td>\n",
       "      <td>29.9671</td>\n",
       "      <td>29.4886</td>\n",
       "      <td>29.9029</td>\n",
       "      <td>148614774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-13</th>\n",
       "      <td>30.0929</td>\n",
       "      <td>30.1329</td>\n",
       "      <td>29.1571</td>\n",
       "      <td>29.7314</td>\n",
       "      <td>151472335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Close     High      Low     Open     Volume\n",
       "Symbol Date                                                     \n",
       "AAPL   2010-01-01  30.1046  30.4786  30.0800  30.4443          0\n",
       "       2010-01-04  30.5729  30.6429  30.3400  30.5000  123432050\n",
       "       2010-01-05  30.6257  30.7986  30.4643  30.6843  150476004\n",
       "       2010-01-06  30.1386  30.7471  30.1071  30.6257  138039594\n",
       "       2010-01-07  30.0829  30.2857  29.8643  30.2400  119282324\n",
       "       2010-01-08  30.2829  30.2857  29.8657  30.0571  111969081\n",
       "       2010-01-11  30.0157  30.4286  29.7786  30.4243  115557365\n",
       "       2010-01-12  29.6743  29.9671  29.4886  29.9029  148614774\n",
       "       2010-01-13  30.0929  30.1329  29.1571  29.7314  151472335"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot include dtype 'M' in a buffer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-5fe1a4e58ddc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# How do we align the existing prices in adj_close with our new set of dates?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;31m# All we need to do is reindex close using all_weekdays as the new index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mclose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_weekdays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;31m# Reindexing will insert missing values (NaN) for the dates that were not present\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mreindex\u001b[1;34m(self, index, **kwargs)\u001b[0m\n\u001b[0;32m   2679\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'reindex'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2680\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2681\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2682\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2683\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'fillna'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mreindex\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3021\u001b[0m         \u001b[1;31m# perform the reindex on the axes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3022\u001b[0m         return self._reindex_axes(axes, level, limit, tolerance, method,\n\u001b[1;32m-> 3023\u001b[1;33m                                   fill_value, copy).__finalize__(self)\n\u001b[0m\u001b[0;32m   3024\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3025\u001b[0m     def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_reindex_axes\u001b[1;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[0;32m   3034\u001b[0m             \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3035\u001b[0m             new_index, indexer = ax.reindex(labels, level=level, limit=limit,\n\u001b[1;32m-> 3036\u001b[1;33m                                             tolerance=tolerance, method=method)\n\u001b[0m\u001b[0;32m   3037\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3038\u001b[0m             \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py\u001b[0m in \u001b[0;36mreindex\u001b[1;34m(self, target, method, level, limit, tolerance)\u001b[0m\n\u001b[0;32m   1910\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1911\u001b[0m                 \u001b[1;31m# hopefully?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1912\u001b[1;33m                 \u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_tuples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1913\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1914\u001b[0m         if (preserve_names and target.nlevels == self.nlevels and\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py\u001b[0m in \u001b[0;36mfrom_tuples\u001b[1;34m(cls, tuples, sortorder, names)\u001b[0m\n\u001b[0;32m   1193\u001b[0m                 \u001b[0mtuples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtuples\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1195\u001b[1;33m             \u001b[0marrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtuples_to_object_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtuples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1196\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtuples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1197\u001b[0m             \u001b[0marrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_object_array_tuples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtuples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/src/inference.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.tuples_to_object_array\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot include dtype 'M' in a buffer"
     ]
    }
   ],
   "source": [
    "# Getting just the adjusted closing prices. This will return a Pandas DataFrame\n",
    "# The index in this DataFrame is the major index of the panel_data.\n",
    "close = data['Close']\n",
    "\n",
    "# Getting all weekdays between 01/01/2000 and 12/31/2016\n",
    "all_weekdays = pd.date_range(start=start_date, end=end_date, freq='B')\n",
    "\n",
    "# How do we align the existing prices in adj_close with our new set of dates?\n",
    "# All we need to do is reindex close using all_weekdays as the new index\n",
    "close = close.reindex(all_weekdays)\n",
    "\n",
    "# Reindexing will insert missing values (NaN) for the dates that were not present\n",
    "# in the original set. To cope with this, we can fill the missing by replacing them\n",
    "# with the latest available price for each instrument.\n",
    "close = close.fillna(method='ffill')\n",
    "\n",
    "close.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-01-01', '2010-01-04', '2010-01-05', '2010-01-06',\n",
       "               '2010-01-07', '2010-01-08', '2010-01-11', '2010-01-12',\n",
       "               '2010-01-13', '2010-01-14',\n",
       "               ...\n",
       "               '2016-12-19', '2016-12-20', '2016-12-21', '2016-12-22',\n",
       "               '2016-12-23', '2016-12-26', '2016-12-27', '2016-12-28',\n",
       "               '2016-12-29', '2016-12-30'],\n",
       "              dtype='datetime64[ns]', length=1826, freq='B')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_weekdays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>SPY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1825.000000</td>\n",
       "      <td>1825.000000</td>\n",
       "      <td>1825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>79.413167</td>\n",
       "      <td>37.118405</td>\n",
       "      <td>164.674986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.302440</td>\n",
       "      <td>10.814263</td>\n",
       "      <td>37.049846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>27.440000</td>\n",
       "      <td>23.010000</td>\n",
       "      <td>102.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>55.460000</td>\n",
       "      <td>27.840000</td>\n",
       "      <td>131.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.440000</td>\n",
       "      <td>33.030000</td>\n",
       "      <td>165.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>103.120000</td>\n",
       "      <td>46.110000</td>\n",
       "      <td>201.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>133.000000</td>\n",
       "      <td>63.620000</td>\n",
       "      <td>227.760000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL         MSFT          SPY\n",
       "count  1825.000000  1825.000000  1825.000000\n",
       "mean     79.413167    37.118405   164.674986\n",
       "std      28.302440    10.814263    37.049846\n",
       "min      27.440000    23.010000   102.200000\n",
       "25%      55.460000    27.840000   131.280000\n",
       "50%      78.440000    33.030000   165.220000\n",
       "75%     103.120000    46.110000   201.990000\n",
       "max     133.000000    63.620000   227.760000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the MSFT timeseries. This now returns a Pandas Series object indexed by date.\n",
    "msft = close.loc[:, 'MSFT']\n",
    "\n",
    "# Calculate the 20 and 100 days moving averages of the closing prices\n",
    "short_rolling_msft = msft.rolling(window=20).mean()\n",
    "long_rolling_msft = msft.rolling(window=100).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19254f2a978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot everything by leveraging the very powerful matplotlib package\n",
    "fig, ax = plt.subplots(figsize=(16,9))\n",
    "\n",
    "ax.plot(msft.index, msft, label='MSFT')\n",
    "ax.plot(short_rolling_msft.index, short_rolling_msft, label='20 days rolling')\n",
    "ax.plot(long_rolling_msft.index, long_rolling_msft, label='100 days rolling')\n",
    "\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Adjusted closing price ($)')\n",
    "ax.legend()\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "12px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
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